Designing Deliberative Democracy for Deeply Divided Places: Strengthening the Legitimacy of Mini-Public Decision-Making
Bibliographic record
Abstract
A warm welcome to ISPP 2018 in San Antonio, Texas.This is ISPP's 41 st international conference.The multidisciplinary study of the intersection of politics and psychology remains incredibly important in a world where national boundaries, citizenship, migration, and trade relations are salient and divisive in ways familiar from history but thought to be of a bygone era.This year's conference encompasses the core themes of "Beyond Borders and Boundaries: Perspectives from Political Psychology."Our program reflects these topics and covers many others from a range of disciplinary and methodological perspectives, adding the now distinctive ISPP edge of cross-national and cross-cultural comparisons.As you will see in the conference program, it will be a busy three days!There are over 350 registered delegates participating in 63 panels, 4 roundtable discussions, 7 other plenary or workshop sessions, and presenting 73 posters.In plenary sessions, Professor Eva G. T. Green will give the ISPP Presidential Address at 5 pm on Wednesday, and Professor Stephen Wright will give keynote addresses at 12 pm on Thursday, and then Professors Jack Citrin and Don Haider-
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.085 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".